Non-invasive prediction of risk for sudden cardiac death

ABSTRACT

A method and apparatus for the quantitative determination of an individual&#39;s risk for sudden cardiac death (SCD) is described. Risk stratification is accomplished (and may have a sensitivity and specificity of greater than about 90%) by determining the presence in any individual being tested for SCD risk of sequences identified herein to correlate quantitatively with SCD risk. Both the number of such sequences present and their alignment scores (similarity) with the SCD risk sequence ensemble are used to calculate quantitative SCD risk.

CROSS REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. application Ser. No.15/693,730, filed on Sep. 1, 2017 which is a divisional of U.S. patentapplication Ser. No. 15/035,122, filed May 6, 2016, U.S. Pat. No.9,775,535 which is a national phase entry under 35 U.S.C. § 371 ofInternational Application No. PCT/US2014/064577 filed Nov. 7, 2014,published in English, which claims priority from U.S. Provisional PatentApplication No. 61/901,800, filed Nov. 8, 2013, all of which areincorporated herein by reference.

FIELD OF THE TECHNOLOGY

The present technology relates to quantitative identification ofindividuals at risk for sudden cardiac death (SCD) and may involve riskstratification and identification of individuals at risk for SCD. Moreparticularly, the present technology may relate to a noninvasiveapparatus, system, device and method, such as using digitizedunprocessed data from a standard resting electrocardiogram (ECG), toaccurately, rapidly, and easily identify in a quantitative mannerindividuals at risk for sudden cardiac death with high sensitivity andhigh specificity.

BACKGROUND OF THE TECHNOLOGY

Sudden cardiac death (SCD) is considered the unexpected death due tocardiac causes of persons with known or unknown cardiac disease, with nounderlying cause for death. SCD occurs within a short period of time,for example, generally within one hour, following the onset of symptoms(if any symptoms are encountered).

SCD is a major public health problem as it has reached epidemicproportions, responsible for at least 325,000 deaths per year in theUnited States alone. (See Goldberger. Circulation. 2008; 118:000-000;Zipes, D. P., et al., ACC/AHA/ESC 2006 Guidelines for Management ofPatients With Ventricular Arrhythmias and the Prevention of SuddenCardiac Death—Executive Summary. Circulation, 2006: p. CIRCULATIONAHA.106.178104). SCD is the second leading cause of death in the U.S.,responsible for slightly less deaths than myocardial infarction. Despitedecreasing incidence of cardiac deaths secondary to improved medicaltreatment and percutaneous and surgical revascularization, SCD continuesto represent about half of all cardiac deaths. (See Ezekowitz. AnnIntern Med. August 2007; 21; 147(4):251-62).

Most cases of SCD are related to cardiac ventricular arrhythmias(ventricular tachycardia, or VT). Coronary heart disease is associatedwith the largest number of SCDs. Acute coronary syndrome (ACS) can leadto malignant arrhythmias that are the result of ischemia. Additionally,coronary artery disease (CAD) may lead to microscopic or macroscopicscar formation that can represent substrate for malignant arrhythmias.

Other cardiac diseases, that put patients at increased risk for SCD,include heart failure, cardiomyopathy, left ventricular hypertrophy(LVH), myocarditis, hypertrophic cardiomyopathy, congenital coronaryartery anomalies, and myxomatous mitral valve disease. Additionally, thepresence of channelopathies such as Brugada syndrome and congenitalheart disease or acquired long QT syndrome, idiopathic ventricularfibrillation (VF), Arrhythmogenic Right Ventricular Cardiomyopathy(ARVC), catecholaminergic VT, and Wolff-Parkinson-White (WPW) syndromeincrease the risk of SCD.

Implantable cardioverter-defibrillator (ICD) therapy has significantlydecreased mortality in high-risk patients, but has done little in termsof affecting overall rates of SCD nationally. (See Bardy. N Engl J Med.2005 Jan. 20; 352(3):225-37). This is explained by the fact thattwo-thirds of patients suffering SCD are in low or intermediate riskgroups, resulting in the greatest absolute number of patient deaths (5).However, multiple trials have repeatedly demonstrated that patients inlow and intermediate risk groups do not benefit from prophylactic ICDs,demonstrating the lack of sensitivity and specificity of contemporarymethods used to stratify SCD risk. The ability to prevent SCD using ICDsis of great importance, as it demonstrates the critical need for ahighly sensitive and specific method for identifying individuals at riskfor SCD.

Based on the foregoing, the identification of individuals presentlyconsidered to be at low or intermediate risk for SCD—but in reality areat high risk—continues to be a major public health concern.

Presently available techniques for the identification of individuals atrisk for SCD include clinical history, (e.g., history of congestiveheart failure (CHF), decreased left ventricular ejection fraction(LVEF), prior myocardial infarction, Holter monitoring, heart ratevariability analysis, signal averaged electrocardiography (SAECG),microvolt T-wave alternans analysis, ambulatory ECG monitoring,metabolic factors and/or parasympathetic tone, heart rate turbulencestudies, baroreceptor sensitivity studies and the presence of myocardialscar as detected using magnetic resonance imaging (MRI). Severelyreduced LVEF and the presence of advanced CHF (class III or IV)currently serve as the main identifiers for patients at high-risk forSCD and, therefore, for identifying who may benefit from ICD therapy andoptimal medical management. (See Epstein. Heart Rhythm. 2008 June;5(6):934-55; Chugh. Nat Rev Cardiol. 2010 June 7 (6): 318-326, availableat http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3052394/). However, nopresently available technique, including those listed above, alone or incombination, has clinically acceptable sensitivity or specificity forthe identification of individuals at risk for SCD. Detailed discussionof some exemplary techniques is provided herewith.

Left Ventricular Ejection Fraction AND CLASS III or IV CHF

Left ventricular ejection fraction (LVEF), as evaluated by one of manymodalities, and the presence of class III or class IV CHF are the twomajor predictors of SCD. They are presently the two principleindications used to determine which patients are candidates for ICDplacement. [Of course, a history of a prior SCD event is an absoluteindication for ICD placement.] (See Epstein. Heart Rhythm. 2008 June;5(6):934-55; Rouleau. J Am Coll Cardiol 1996; 27:1119-27). Assessment ofejection fraction (EF) has multiple advantages such as accessibility,ease of use, and reproducibility and has been the major determinant ofpatients who are considered for prophylactic ICD implantation. Severaltrials have found that an LVEF≤35%, particularly with symptoms of heartfailure (CHF), have served as the marker for identifying high-riskpatients. (Bardy. N Engl J Med. 2005 Jan. 20; 352(3):225-37).Conversely, multiple studies have found an LVEF≥40% is not an accuratemarker for those at increased risk for SCD. This finding suggests thatEF may be less useful as a marker for SCD once the EF is greater than40% on a population basis. (See Ikeda. J AmColl Cardiol 2006;48:2268-74).

Randomized clinical trials concerning prophylactic ICD implantation haveevaluated patients with depressed LVEF. The implications of thesestudies in the treatment of SCD are limited since most cases of SCD donot occur in patients with low LVEF. (See Zipes, D. P., et al.,ACC/AHA/ESC 2006 Guidelines for Management of Patients With VentricularArrhythmias and the Prevention of Sudden Cardiac Death—ExecutiveSummary. Circulation, 2006: p. CIRCULATION AHA.106.178104). Studies suchas multicenter automatic defibrillator implantation trial I (MADIT I),MADIT II and sudden cardiac death in heart failure trial (SCD-HeFT) haveall shown significant reductions in arrhythmic and overall mortalitywith ICD therapy in patients with severely decreased LVEFs. However, themajority of patients who were evaluated, and showed benefit from theprophylactic ICD implantation, had LVEFs≤25%, limiting the ability toextrapolate this data to the low and intermediate risk groups.

Analysis of the multicenter unsustained tachycardia trial (MUSTT)demonstrates that LVEF does not, in fact, represent the greatest risk oftotal and arrhythmic death. New York Heart Association (NYHA) class,history of heart failure, non-sustained VT, enrollment as inpatient, andatrial fibrillation all portended greater risk as individual markers.Patients with LVEF<30% but with no other risk factors may have a lowerpredicted mortality risk than patients with LVEF>30% as well as otherrisk factors. Risk of SCD in patients with cardiomyopathy depends onmultiple variables in addition to LVEF and may be further elucidatedusing other methods. (See Salerno-Uriarte. J Am Coll Cardiol 2007;50:1896-904). Over all, EF<35% and/or the presence of class III or classIV CHF are important criteria (although present in only a minority ofSCD patients) for ICD placement. However, although they are the bestpresently available methods for SCD risk prediction, they suffer fromunacceptably low sensitivity and specificity.

Signal-Averaged Electrocardiogram

In patients with VT, areas of scar may result in slow conduction andprolonged activation of segmental regions of the ventricle. This slowingmay manifest itself as ventricular late potentials which arelow-amplitude signals that occur after the end of the QRS complex andare thought to reflect slow and fragmented myocardial conduction. (SeeSimson. Am J Cardiol 1983; 51:105-112). Late potentials have beencorrelated with abnormal signals found during electrophysiologicalstudies in segmental sections of the endocardium and representslowly-activated tissue that can represent substrate for reentry. (SeeSimson. Am J Cardiol 1983; 51:105-112).

Signal averaged electrocardiography (SAECG) is a technique wheremultiple QRS complexes are digitized, averaged, filtered, and furtherprocessed with spectral analysis to facilitate late potential analysis.The sensitivity and specificity of an abnormal SAECG for the predictionof SCD or arrhythmic events has been reported to vary from 30% to 76%and the specificity from 63% to 96% (Bailey. J Am Coll Cardiol 2001;38:1902-1911). Conversely, the negative predictive value is high,exceeding 95%, also reflecting the low prevalence of SCD.

There are limited data evaluating the prognostic value of SAECG inpatients with an LVEF greater than 35% and what data exists has beeninconsistent (Ikeda. J AmColl Cardiol).

Microvolt T-Wave Alternans

Microvolt T-wave alternans (MTWA) is a technique that was developed toidentify instability of ventricular repolarization during exercise. Thisinstability can lead to dispersion of ventricular refraction, and hasbeen promoted as another methodology for risk stratification for SCD.(See Pastore. Circulation 1999; 99:1385-94; Verrier, R. L., et al.,Microvolt T-wave alternans physiological basis, methods of measurement,and clinical utility—consensus guideline by International Society forHolter and Noninvasive Electrocardiology. J Am Coll Cardiol, 2011.58(13): p. 1309-24).

A large meta-analysis has shown prognostic value of a negative MTWA testin post-myocardial infarction (post-MI) patients with reduced ejectionfraction, with the strength of the test resulting mainly from a veryhigh negative predictive value (See Gehi. J Am Coll Cardiol 2005;46:75-82). Additional studies evaluating the prognostic value of MTWAhave been inconsistent (See Salerno-Uriarte. J Am Coll Cardiol 2007;50:1896-904; J Am Coll Cardiol 2007; 50:1896-904).

In a large Italian study, more than 400 patients were tested for MTWAand followed over 18-24 months revealing a negative predictive value of97%. However, the patients who tested positive for MTWA represented agroup who had concomitantly been diagnosed with either non-ischemiccardiomyopathy, NYHA II/III CHF, or a LVEF less than 40%, representing agroup of patients already at high risk for SCD and adding little benefitto a low or intermediate risk population. The evidence for usefulness ofMTWA in this population is not well-established and has generally beenlimited by poor positive predictive values due to low prevalence (SeeIkeda. J AmColl Cardiol 2006; 48:2268-74).

Ambulatory ECG Monitoring

The detection of ventricular arrhythmias (including prematureventricular contractions (PVCs) and non-sustained ventriculartachycardia (NSVT)) using ambulatory ECG monitoring in patients withleft ventricular dysfunction following myocardial infarction isassociated with an increased risk for mortality (See Bigger. Circulation1984; 69:250-8).

However, there is no significant increased value of ambulatory ECGmonitoring for risk-stratification in high-risk patients. (See Bardy. NEngl J Med. 2005 Jan. 20; 352(3):225-37; Moss. N Engl J Med, Vol. 346,No. 12).

Given currently available data, when evaluating the risk of SCD inpatients without severe LV systolic dysfunction, the value of ambulatoryECG testing is inconclusive and the low positive predictive value ofidentifying NSVT in this patient population may limit its clinicalutility. (See Maggioni. Circulation 1993; 87:312-22).

Heart Failure

The clinical syndrome of congestive heart failure (CHF) can contributeto arrhythmogenesis in patients with ventricular dysfunction and canincrease mortality in patients regardless of LVEF.

Patients with NYHA Class I and II symptoms have been shown to have lowoverall death rates. However, 67% of total deaths were due to SCD. Incontrast, among studies with a mean functional Class IV, there was ahigh total mortality, but the fraction of SCD was only 29% as theincidence of progressive pump failure increased. (See Goldberger.Circulation. 2008; 118:000-000). This paradox continues to have majorimplications on the current utility of ICDs in the low and intermediatepopulation.

Heart failure classification is often dynamic in nature depending on themodality at the time, volume status, medications used at the time, andother comorbid conditions that could influence functional status,thereby limiting its utility.

Metabolic Factors

Factors related to ventricular arrhythmias and SCD include serumcatecholamine levels and electrolyte imbalances. Manifestations ofneurohormonal activation, such as hyponatremia and increased plasmanorepinephrine, renin, and natriuretic peptide levels, have been foundto be predictive of mortality as well. (See Pratt. Circulation 1996;93:519-524).

Autonomic Control

Autonomic imbalance has been implicated in SCD, possibly due to reducedvagal tone and sympathetic enhancement, favoring the formation oflife-threatening arrhythmias.

Markers of autonomic control, such as heart rate variability (HRV),baroreflex sensitivity (BRS), and heart rate turbulence (HRT), have beenfound to have independent and in some cases additive prognostic valuefor SCD. While this effect is more prominent in patients with a reducedejection fraction, some trials showed significant risk even for patientswith relatively preserved EF. (See Lombardi. Cardiovasc Res (2001) 50(2): 210-217).

Numerous studies have explored the prognostic value of HRV parametersfor predicting outcomes in postinfarction patients and have consistentlyshown depressed HRV is associated with increased mortality. (SeeLombardi. Cardiovasc Res (2001) 50 (2): 210-217). However, dataregarding the prognostic significance of HRV for predicting SCD inpatients with ischemic heart disease is lacking, and serves no role inrisk-stratification. All data to date concerning autonomic control inpatients with a relatively preserved LVEF has not proven significant.(See De Ferrari. J Am Coll Cardiol 2007; 50:2285-90).

Based on the foregoing, at present there are no methods with acceptablesensitivity or specificity to be clinically useful in the identificationof people at risk for SCD. There is a critical need for a device able toidentify individuals at risk for SCD (independent of any underlyingpathology) with high sensitivity and specificity. This is particularlytrue because there are presently available technologies able to preventSCD in individuals at risk (e.g., ICDs). There is also a great need toaccurately identify patients in currently recognized high-risk groupsfor SCD who would not benefit (and possibly suffer) from high-cost ICDplacement.

Point of convention: In the literature, the term Sudden Cardiac Death(SCD) refers to 1) the occurrence of those ventricular arrhythmiaswhich, if not immediately and successfully treated (e.g., by an externalor implantable cardioverter-defibrillator) lead to death or 2) the deathof an individual from such a ventricular arrhythmia. In contrast, someauthors refer to the occurrence of these most often lethal ventriculararrhythmias as Sudden Cardiac Arrest (SCA), and use the term SCDspecifically in those cases in which the arrhythmic event results indeath. Throughout the remainder of this specification, the first andmore commonly used convention is intended.

BRIEF SUMMARY OF THE TECHNOLOGY

The present technology relates to a non-invasive risk stratificationmethodology for predicting an individual's risk for SCD.

A first aspect of the present technology relates to a method fordetermining electrocardiogram (ECG) sequences (e.g., continuous,adjacent segments [in time] of digital ECG voltages) specificallyindicative of a risk for sudden cardiac death (SCD). The method mayinclude receiving a first plurality of ECG measurements, e.g., digitalmeasurements, taken from individuals who have no history of heartdisease and no history of SCD, receiving a second plurality of ECGmeasurements, e.g., digital measurements, taken from individuals whohave a history of heart disease (such as representing a variety ofetiologies, including ischemic heart disease, cardiomyopathies,congestive heart failure, etc.) but no history of SCD, and receiving athird plurality of ECG measurements, e.g., digital measurements, takenfrom individuals who have a history of SCD (with or without a priorhistory of heart disease). One or more processor(s) may identify thoseECG sequences unique to the third plurality of ECG measurements, but notthose sequences obtained from normal or non-SCD heart diseaseindividuals. The identified ECG sequences are present in the thirdplurality of ECG measurements but absent from the first and secondpluralities of ECG measurements. The processor, after isolating digitalECG sequences from any standard resting ECG machine, may determine theECG sequences indicative of the risk for SCD based on the identified ECGsequences. Each ECG measurement may be obtained from a standard resting12-lead ECG machine.

In some cases, the method may include preprocessing, with apreprocessor, such as a processor that extracts digital ECG data fromstandard ECG machines. The preprocessor may then filter the digital ECGdata to remove noise contaminating the data. In addition, it mayconstruct or compute the first and second derivatives of the filtereddata for additional risk sequence extraction/isolation. The digital ECGmeasurement may be denoised by at least one of the following methods:digital filtering and a combination of wavelet denoising methods. Thenoise may arise from all of the following sources: electrical noise,mechanical noise, respiration, white noise, movement artifact, andbaseline drift. In some cases, the first and second derivativescalculated from this data are generated.

The processor may identify and isolates those digital sequences uniqueto individuals who have experienced SCD

In some cases, the processor may identify and isolate those ECGsequences unique to the third plurality of ECG measurements by‘subtracting’ (such as by methods discussed herein) from the completeECG sequences present in patients who have experienced SCD (or SCA, asdescribed above) all of the digital ECG sequences present in individualswith or without heart disease that have not experience SCD. Theprocessor may identify and isolate those ECG sequences relatively uniqueto the third plurality of ECG measurements (sequences). This isaccomplished by analyzing all of the digital ECG sequences present inthe third pluralities of ECG measurements; aligning these ECG sequenceswith respect to all of the ECG sequences present in the first and secondplurality of ECG measurements, and calculating an alignment score (orequivalently, similarity score) for all of the sequences of the thirdplurality to the combined sequences of the first and second plurality.All sequences of the third plurality with low alignment or similarityscores to the combined sequences of the first and second pluralitygroups are then isolated. These identified and isolated sequences arethen defined as putative SCD-specific risk sequences.

Further analyses of these putative risk sequences may be performed byone or more processors in order to determine the actual SCD risksequences and the optimal combination/collection of risk sequences touse in order to determine patient risk for SCD. The processor performsthese operations using a plurality of local and global optimizationmethods. This is using a separate set of digital ECG sequencesrepresentative but unique from the above patient groups (pluralities).In particular, the processes of optimization are done with data not usedin the identification and isolation of putative SCD risk sequences.

A beneficial application of the present technology is a method, such asin a processor or other processing apparatus, for determining anindividual's risk for sudden cardiac death (SCD), such as with highsensitivity and specificity (e.g., greater than about 90%). The methodmay include extracting (directly or wirelessly) digital ECG data fromany individual being tested using any standard ECG device, anddetermining the number and accuracy of alignment (alignment orsimilarity score) of the ECG sequence present in the patient undergoingSCD risk analysis with the optimal SCD risk sequence ensemble previouslyconstructed (as described above). Determining the number of SCD risksequences and the accuracy of alignment (similarity) of these sequenceswith members of the optimized SCD risk sequence ensemble, SCD risk isdetermined quantitatively with high sensitivity and specificity (e.g.,greater than 90%).

The foregoing summary is illustrative only and is not intended to be inany way limiting. In addition to the illustrative aspects, embodiments,and features, described above, further aspects, embodiments, andfeatures will become apparent by reference to the drawings and thefollowing detailed description.

BRIEF DESCRIPTION OF THE DRAWINGS

The present technology is illustrated by way of example, and not by wayof limitation, in the figures of the accompanying drawings, in whichlike reference numerals refer to similar elements including:

FIG. 1 is an illustrative overview of the present technology includingan SCD risk sequences isolator and an SCD risk predictor.

FIG. 2 shows an example clinical application of the SCD risk predictorof FIG. 1 in accordance with the present technology.

FIG. 3 illustrates a flow chart of an overall process performed by theSCD risk sequences isolator and the SCD risk predictor in accordancewith the present technology.

FIG. 4 shows a component diagram of the SCD risk sequences isolator.

FIG. 5 illustrates the clinical groups used to obtain the clinical datafor the construction of the optimized SCD risk sequence ensemble as wellas the data to calculate the sensitivity and specificity of the SCD riskpredictor.

FIG. 6 is a block diagram of an example implementation of the SCD risksequences isolator.

FIG. 7 is a block diagram of the preprocessor of the SCD risk sequencesisolator.

FIG. 8 is a block diagram of the processor of the SCD risk sequencesisolator.

FIG. 9 is a block diagram of the SCD risk predictor and itsimplementation.

DETAILED DESCRIPTION

Before the present technology is described in further detail, it is tobe understood that the technology is not limited to the particularexamples described herein, which may vary. It is also to be understoodthat the terminology used in this disclosure is for the purpose ofdescribing only the particular examples discussed herein, and is notintended to be limiting.

The following description provides specific details of aspects of thetechnologies detailed herein. The headings and subheadings providedherein are for convenience and ease of reading only.

1. Overview

The present technology described herein generally relates toquantitatively identifying individuals at risk for Sudden Cardiac Death(SCD), which may also be termed Sudden Cardiac Arrest (SCA), usingnoninvasive methods. Such noninvasive mechanisms include identifying andisolating digital electrocardiogram (ECG) subsequences, which may beunderstood herein as measured cardiac related biopotential signals notstrictly requiring a 12-lead ECG, which correlate with the occurrence oflethal ventricular arrhythmias or SCD based on information obtained fromclinical studies. The digital ECG subsequences identified as such may bereferred hereinafter as SCD risk sequences.

Specifically, to identify the SCD risk sequences, the noninvasivemechanisms discussed herein analyze digital ECG data from survivors ofSCD, as well as individuals with and without heart disease who have notexperienced SCD. A survivor of SCD may be defined as an individual whohas experienced a deadly ventricular arrhythmia or SCD (or SCA), but hassurvived the event as a result of cardioversion. Examples ofcardioversion may include applying an electrical shock to an individualvia an external cardiac defibrillator or an implanted cardioverterdefibrillator (ICD).

The digital ECG data may, for example, be obtained from a standard ECGmachine, such as a standard resting 12-lead ECG machine. Once thedigital ECG data is obtained, the noninvasive mechanisms may identifyand isolate SCD risk sequences from the digital ECG data.

The optimal collection of SCD risk sequences used for quantitative riskprediction is constructed by the processor through the use of a varietyof local and global optimization techniques.

The noninvasive mechanisms discussed herein also predict an individual'squantitative risk for SCD using the optimal set of SCD risk sequences.For instance, the present technology may detect the presence of the SCDrisk sequences in an individual's ECG. Based on the quantitativepresence or absence of SCD risk sequences in the individual's digitalECG data, the present technology may calculate the individual's risk forSCD and output a scalar number that reflects the individual'squantitative risk for SCD. The quantitation is performed based upon thenumber of risk sequences identified in the ECG data derived from theindividual being tested, as well as the alignment score (similarity) ofthe patient ECG data to the SCD risk sequences.

By way of illustration, FIG. 1 shows an example operating environment ofthe present technology. The present technology may include two distinctapparatuses—(1) an SCD risk sequences isolator 102 and (2) an SCD riskpredictor 104. The SCD risk sequences isolator 102 may identify andisolate, from clinical studies, SCD risk sequences present in survivorsof SCD which correlate with the occurrence of lethal ventriculararrhythmias or SCD. The optimal collection of risk sequences is createdfrom the putative SCD risk sequences by the risk sequence isolator usinglocal and global optimization methods.

For instance, the predictor 104 may be implemented in an individualapparatus, e.g., an ECG device, a general monitoring device, aHolter-type recording device, a PC chip, an ICD, or ATP-ICD(antitachycardia pacing ICD), SCD ablation equipment, etc. or as aself-contained unit.

The isolator 102 may identify, isolate, and optimally group the SCD risksequences, such as in the form of data representing those sequences. Theisolator 102 may require as input (e.g., digital ECG data) collectedfrom individuals (such as a clinical study) in order to identify andisolate the SCD risk sequences—those digital ECG sequences thatcorrelate with the occurrence of SCD. Once identified and isolated insuch a training process, these sequences are further optimized into acollection of risk sequences. This optimized collection of SCD risksequences may be programmed into the memory of the SCD risk predictor104. It is therefore apparent that the isolator 102 may be employed oncefor the identification, isolation, and construction of the optimalcollection (using local and global optimization techniques) of SCD risksequences derived from clinical study.

The SCD risk predictor 104 may be then preprogrammed with thosesequences identified by one or more clinical studies. The predictor 104may then predict an individual's SCD risk level by searching for thepresence of these SCD risk sequences in an individual's measured digitalECG data. Quantitative risk prediction is performed by determining thenumber of risk sequences present in the test patient, as well as theaccuracy of alignment (similarity) of the sequences in the rest patientto the risk sequences in the optimized risk sequence ensemble. In someexamples, the predictor 104 may be used once or on multiple occasions inany given individual or many different individuals. Thus, thepreprogrammed risk predictor 104 can be a tool in clinical practice. Forinstance, as illustrated in FIG. 2, the predictor 104 may operate inconcert with a standard ECG device 145 measures the digital ECG data ofan individual 115 via electrodes 130. The predictor 104 may predict theindividual's risk for SCD and display on a screen the predicted level ofthe risk for SCD, along with accuracy measures (e.g., specificity andsensitivity) of such predictions.

FIG. 3 is a flowchart that illustrates an embodiment of a method ofoperation of the present technology. Methods illustrated in the flowchart of FIG. 3 and other flowcharts discussed herein may be executed byprocessors. In some examples, methods illustrated in each flow chart maybe carried out periodically, continuously, as needed, as triggered, orin another manner. Each method may include one or more operations,functions, or actions as illustrated by one or more of the blocks. Ablock may represent a process of information, a transmission ofinformation, or a combination thereof.

In a flowchart, although the blocks are illustrated in a sequentialorder, these blocks may also function in parallel or in a differentorder than those described herein, depending on the functionalitiesinvolved. Also, the various blocks may be combined into fewer blocks,divided into additional blocks, sub-blocks, or omitted based upon thedesired implementation. Furthermore, blocks illustrated in various flowcharts may be combined with one another, in part or in whole, based onthe functionalities involved.

Referring to FIG. 3, at block 302, the isolator 102 may identify,isolate, and optimally group SCD risk sequences from digital ECGsequences unique to survivors of SCD (SCA). The SCD risk sequences mayrepresent sequences that are responsible for the occurrence of SCD. Atblock 304, the predictor 104 may determine any individual's risk forSCD, where the individual may or may not be a survivor of SCD. Thepredictor 104 may obtain the individual's digital ECG measurement fromany ECG device 145 as illustrated in FIG. 2. The predictor 104 maysearch for the presence of SCD risk sequences with respect to theindividual's ECG measurement, an determine the accuracy of alignment(similarity) between the patient's identified risk sequences and thoseused to preprogram the isolator.

More details regarding implementation of the isolator 102 and thepredictor 104 are provided herewith.

2. SCD Risk Sequence Isolator 2.1 General Operation of the Isolator

As described earlier, the processes of the isolator 102 may include theidentification or isolation of sequences that correlate with theoccurrence of SCD. This may be performed in several steps.

Unprocessed digital ECG data can be obtained either directly from one ormore ECG leads, or from any ECG-capable device prior to processing ofthe input data. This is the desired format of data used by both theisolator and the predictor. At a first step, the isolator may evaluateinput (e.g., digital ECG data) obtained in a clinical study. This datamay include standard, resting 12-lead digital ECG data obtained from anumber of groups of individuals (e.g., three groups of individuals) suchas the following:

-   1. one group of individuals with no history of heart disease and no    known risk factors for SCD;-   2. one group of individuals with heart disease (of a variety of    etiologies), but no history of SCD, and-   3. one group of individuals with or without a history of heart    disease that have experienced SCD (SCA).

All 12 leads of digital ECG data from each person in the study/studiesmay be used as input for the isolator 102. This digital data is obtainedby the isolator either directly from the ECG leads, or from the ECGmachine prior to any preprocessing or processing. In either case, leadplacement is not modified for the study from usual ECG lead placement.The isolator preprocessor can operate upon 12 digital ECG signals (onecorresponding to each ECG lead) from each individual in the clinicalstudy/studies.

At a second step, the digital ECG data obtained from each patient andstored in the isolator 102 memory can be denoised by its preprocessor.Complete systematic denoising is required for all digital ECG data. Thepreprocessor may perform denoising first by employing digital filtertechniques. Finite Impulse Response (FIR) filter techniques may be usedto optimally perform this initial denoising function. Denoising may thenbe optimized using wavelet packet techniques. This extensive degree ofdenoising is required to obtain digital ECG data with a very highsignal-to-noise (STN) ratio (minimal loss of significant ECG informationand maximal removal of noise resulting from a variety of sources).

At a third step, for the purpose of enabling additional higher-leveldigital ECG sequence analysis, the preprocessor may compute the firstand second derivatives of the optimally denoised digital data. Analysisof data corresponding to the first and second derivative of the digitalinput data is beneficial since it correlates with critical electricalactivities (e.g., depolarization and repolarization, conductionvelocity, and conduction turbulence) within the heart. Thus, thetransformed data may provide additional sources of potential SCD risksequences.

Subsequently, all of the fully preprocessed digital ECG data (as well asthe first and second derivatives of this data) may then be furtherprocessed by the processor(s) within the SCD risk sequence isolator 102.

The identification and isolation of the SCD risk sequence may then beperformed by the isolator 102 such as in a processing unit.

In such a processing unit, the preprocessed ECG data obtained andderived from those individuals in the study(ies) who have experiencedSCD are completely analyzed. In particular, using alignment techniques,all of the digital ECG sequences present in the groups of patients (withor without a history of heart disease) who have not experienced SCD(SCA) are removed from the collection of ECG sequences present in one ormore patients comprising the group of patients who have experienced SCD(SCA). The actual method used to identify, isolate, and construct theoptimal collection of SCD risk sequences (and subsequently used to trainthe SCD risk predictor) requires three steps:

1) In a sequential manner, every single mathematically possiblealignment between every possible length (of adjacent ECG data points) ofdenoised digital ECG serially beginning at every time point in the ECGdata collection obtained and/or constructed from every patient in thestudy(ies) is abstractly created. The quality of alignment (similarity)of each of these ECG data pairings between the corresponding ECG leadsof all patients in the study(ies) (as well as the derivatives of thisdata) is quantitated determined using a variant of the root mean square(RMS) error method. Determination of those alignments with alignmentvalues above or below certain thresholds allows the identification ofECG segments relatively common to and relatively unique to patientgroups. This information allows the identification and isolation ofsequences relatively unique to patients (with or without heart disease)who have experienced SCD (SCA). This collection of sequences isrelatively free of sequences present in patients with any of a varietyof heart diseases who have not experienced SCD. These sequences arereferred to as ‘putative SCD risk sequences’.

2) Using published values of SCD (SCA) incidence (as a function of time)in individuals with no history of heart disease and patients with heartdisease of a variety of types (i.e., ischemic heart disease, congestiveheart failure, etc.) with or without a prior SCD event (SCA), the timedependent risk of every patient in the study for SCD is calculated.Using local optimization methods, the putative SCD risk sequences whichoptimally correlate with the detailed patient data are identified andisolated. These sequences are referred to as SCD risk sequences (asopposed to putative SCD risk sequences).

3) The collection of SCD risk sequences which best correlate with theabove patient and patient group information and data is then constructedusing global optimization techniques. The optimized collection of SCDrisk sequences contains sequences from each ECG lead as well as thesequences obtained by first and second order differentiation of theinput ECG data.

The sensitivity and specificity of the SCD risk predictor trained by theoptimized SCD risk sequence group or ensemble constructed in any givenclinical trial (the ensemble being constructed from the SCD risksequences identified and isolated in the clinical trial by thepreprocessor followed by global optimization technologies, as describedpreviously) is determined using a separate group of patient—the testpatients—from the clinical trial. As demonstrated in FIG. 5, these testpatients consist of: individuals with no history of heart disease whohave never experiences SCD, individuals with a history of heart diseasewho have never experiences SCD, and individuals (with or without ahistory of heart disease) who have survived SCD (SCA). It is importantto note that the test groups of individuals used to determine thesensitivity and specificity of the SCD risk predictor have never beenpreviously seen or used by the SCD risk predictor. In this manner, thesensitivity and specificity of the SCD risk predictor are determined tobe greater than 90%. These values have been obtained in two separateclinical trials.

2.2 Example Components of Isolator

FIG. 4 is a schematic diagram of an example implementation of theisolator 102. As illustrated, the isolator 102 may include one or moreof the following components: one or more preprocessors such aspreprocessor 160, one or more processors 162, a storage device 142, aninput device 144, and an output device 146. Components of the isolator102 may be communicatively coupled together in either a wired orwireless fashion. In some cases, the methodologies of the processingcomponents may be achieved in a single processor or multiple processors.In one example as illustrated in FIG. 4, the components may be coupledtogether by a system bus 148. Detailed description of each component isas follows.

2.2.1 Preprocessor and Processor of Isolator

The preprocessor 160 and processor 162 may control the functions of theisolator 102. For instance, the preprocessor 160 may perform thefollowing functions, including denoising and the calculation the firstand second order derivatives of the digital input data. The processor162 may perform the following operations, including putative SCD risksequence identification and isolation, SCD risk sequence identificationand isolation, and construction of the optimal ensemble of SCD risksequences. The preprocessor 160 and processor 162 may be of any typeincluding but not limited to a general purpose preprocessor or processorand a special purpose or dedicated preprocessor or processor, e.g., anapplication-specific integrated circuit (ASIC), a digital signalprocessor (DSP), a graphical processing unit (GPU), a floating pointprocessing unit (FPU), and the like. The processor 162 may refer to asingle processor, or a collection of processors of the same type orvarious types, which may or may not operate in a parallel-processingmode.

The processor 162 may communicate with other components of the isolator102. In one example, the processor 162 may execute computer-readableinstructions or other instructions stored in the storage device 142. Theprocessor 162 may read and write the data during execution of thecomputer-readable instructions. In another example, the processor 162may act upon input signals provided by the input device 144.

2.2.2 Storage Device of Isolator

The storage device 142 may provide storage for the isolator 102 by usingone or more non-transitory computer-readable media. Thecomputer-readable media may store volatile data, non-volatile data, or acombination thereof. Some computer-readable media may store data for ashort period of time. Other computer-readable media may store datapersistently for a long period of time.

The computer-readable media may include primary storage, secondarystorage, or a combination thereof. The primary storage may be simplyreferred to as memory, which is directly accessed by the processor 162.The secondary storage may be indirectly accessed by the processor 162via the primary storage.

The computer-readable media may be of different types includingrandom-access memory (e.g., SRAM and DRAM), read-only memory (e.g., MaskROM, PROM, EPROM, and EEPROM), non-volatile random-access memory (e.g.flash memory), a magnetic storage medium, an optical disc, a memorycard, a Zip drive, a register memory, a processor cache, a solid statedrive (SSD), and a redundant array of independent disks (RAID), amongother possibilities.

The storage device 142 may store one or more computer-readableinstructions, data, applications, processes, threads of applications,program modules and/or software, which are accessible or executable bythe processor 162 to perform at least part of the herein-describedmethods and techniques.

By way of example, the computer-readable instructions in the storagedevice 142 of the isolator 102 may include logic that identifies andisolates SCD risk sequences.

Examples of data stored in the storage device 142 may include but notlimited to variables, results, data obtained from one or more ECGdevices, the SCD risk sequences, and parameters used to identify the SCDrisk sequences, among other possibilities.

2.2.3 Input Device

The input device 144 may refer to one or more peripheral devicesconfigured to receive information from individuals. The input device 144may communicate such information to other components of the isolator102.

By way of example, the input device 144 may be one or more ECG leads, anECG device, such as a standard resting 12-lead ECG device or device withmore or fewer of such electrode leads. The input device 144 may alsoinclude user input components such as a keyboard, keypad, touch pad,point device, track ball, joystick, voice recognition device,touch-sensitive surface, microphone, digital camera, mouse, buttons,switch, scroll-wheel, scanner, GPS receiver, movement sensor, locationsensor, infrared sensor, optical sensor, Radio Frequency identification(RFID) system, and wireless sensor, among others. In some examples, theinput device 144 may include an external defibrillator or implantablecardioverter-defibrillator (ICD or ATP-ICD).

The input device 144 may provide a number of different types of digitalinput data, such as a digital ECG measurement, an electrogram (EGM)measurement, audio data from a microphone, text data from a keypad,video or image data from a camera, and gesture data from a touchpad,just to name a few. This data may be gathered from clinical studies ongroups of individuals such as with other devices and transferred indigital form to the isolator via the input. This input is used by theisolator for its identification and isolation of SCD risk sequences.

2.2.4 Output Device of Isolator

The output device 146 may communicate one or more outputs of theisolator 102 to the SCD risk predictor 104. The output device 146 mayinclude output components such as a digital output file, a digitaloutput storage device, a visual display, audio transducer, lightindicator, tactile transducer, printer, light bulb, and vibrationgenerator, among others. The output device 146 may provide a number ofdifferent types of output data, such as digital data, visual output viaa display, audio output via a speaker, and tactile output via avibration generator, among others.

By way of example, the output device 146 may be a quantitative SCD riskpredictor. Also, the output device may be a digital storage device. Insome examples, the output device 146 may include one or more audiotransducers in the following forms: a speaker, headset, jack, earphone,and audio output port.

2.3 Example Logic and Methods of Isolator

The isolator 102 may include computer algorithms such ascomputer-readable instructions, ASICs, FPGAs, DSPs, integrated circuits,modules, firmware, or a combination thereof, among other possibilities.These computer algorithms may be implemented in a signal bearingnon-transitory computer-readable storage medium in a variety of forms.The isolator 102 may perform several functions, including identifying,isolating, and/or quantification of SCD risk sequences from patient dataobtained from survivors of SCD (in concert with digital ECG data frompatients who either have or do not have heart disease but have neverexperienced SCD). The isolator 102 may perform only once or be reusedseveral times (upon digital ECG data from patients of different clinicalstudies). The SCD risk sequences identified and isolated by the isolator102 may be transferred and stored by a storage device or by thepredictor 104.

The SCD risk sequence isolator 102 may generally identify, isolate, andoptimally group and organize SCD risk sequences into ensembles. TheseSCD risk sequence ensembles may be used to train any of a variety of SCDrisk predictors.

According to some aspects of the present technology, the isolator 102may be a one-time-only process, that is, the isolator 102 may be usedonly once to identify, isolate and optimally group the SCD risksequences that are present in those individuals of a particular clinicalstudy. These sequences serve as a standard for future risk predictionsby the predictor 104. The isolator 102 may also be reused to identify,isolate and optimally group SCD risk sequences present in theindividuals of a different clinical groups or studies. The isolated SCDrisk sequences obtained by the isolator 102 in any given clinical studymay be stored in the isolator, on a disc, or used to preprogram anygiven predictor 104.

FIG. 6 illustrates an exemplary configuration of the isolator 102. Asillustrated, the isolator 102 may include one or more of the followingunits: a preprocessor 160 for ECG data denoising and data derivativecalculations, and a processor 162 for identifying, isolating andoptimally grouping the SCD risk sequences. Detailed description of eachunit is as follows.

2.3.1 Isolator Preprocessor

FIG. 7 is a schematic illustration of an exemplary configuration of theisolator preprocessor 160. The isolator preprocessor 160 may preprocessdigital ECG measurements, such as those obtained from the clinicalstudy/studies described above. The digital ECG measurements may includeall of the digital information obtained from all leads of any digitalECG's device or from the ECG lead itself. As illustrated in FIG. 7, thedigital ECG measurements of any human subject in the clinicalstudy/studies may be obtained from an ECG device 145. The digital ECGdevice 145 may be a standard resting 12-lead digital ECG device or sucha measurement device of any other number of leads. Alternatively, thedigital ECG device 145 may be a management system, such as the MUSECardiology Information System by GE Healthcare, which stores and managesdigital ECG measurements output by one or more digital ECG devices. Thedigital ECG measurement obtained from an individual may include measuredvoltages obtained from each lead. For instance, as illustrated in FIG.7, the digital ECG measurement extracted from a standard resting 12-leaddigital ECG device for one individual may have twelve sequencesrepresenting digital 12-lead ECG measurements obtained from theindividual. A sequence may represent the voltage measures as a functionof time associated with one of the twelve leads: Lead I, Lead II, LeadIII, Lead aVR, Lead aVL, Lead aVF, Lead V1, Lead V2, Lead V3, Lead V4,Lead V5, and Lead V6. For example, each individual in the TrainingGroups A, B, and C may have twelve sequences. Different individuals mayhave different or similar sequences associated with each lead. Eachdigital ECG measurement may include measurements taken from a period oftime (e.g., approximately 10 seconds, or other suitable time periods).

As illustrated in FIG. 7, the preprocessor 160 may include one or bothof the following subunits: a denoising subunit 170, and a computationalsubunit which calculates the first and second derivitives of thedenoised input data.

The denoising subunit 170 may receive digital ECG measurements, andremove signal noise using a Finite Impulse Response (FIR) digitalfilter. The signal noise may include electrical noise, mechanical noise,respiration-related noise, white noise, movement artifact, and baselinedrift.

Wavelet packet filtering may then performed by the isolator preprocessor160 for further signal denoising. The wavelet filters 174 may useseveral wavelet families at a variety of decomposition levels to furtherdenoise the signals. The wavelet filter 174 may employ entropy methodsto obtain optimal thresholding in order to obtain ideal denoising. Thewavelet filter 174 may include implementation of a discrete wavetransform. Alternatively, the wavelet filter 174 may includeimplementation of a continuous wavelet transform. Parameters associatedwith the continuous wavelet transform may be adjusted eitherautomatically or manually.

Finally, the preprocessor may calculate the first and second derivativecorresponding to the digital data obtained from each ECG lead. Thisinformation is beneficial, since these derivatives are related tovoltage conduction velocity and turbulence. These processes are known tobe associated with the occurrence of SCD.

2.3.2 Isolator Processor

The Isolator Processor (represented schematically in FIG. 8) receives asinput the output of the Preprocessor 160. The Preprocessor exhaustivelydetermines the alignment scores between all preprocessed ECG sequencesfrom all members of each of the three patient groups. The alignmentscore—a measurement of the degree of similarity between two sequences orsubsequences—is determined by a process or processing component (e.g.,alignment Score Subunit 182) of the Isolator Processor. For example, thequantitative measure of sequence alignment may be determined byimplementing a modified, scaled Minkowski metric.

The Isolator uses these alignment scores to isolate those sequencespresent uniquely (relative to Patient Group A) and/or relativelyuniquely (relative to Patient Group B) in the ECG sequences of PatientGroup C. This process of isolation removes from the ECG sequences ofPatient Group C those subsequences present in this patient groupresponsible for normal (Patient Group A) as well as non-SCD related ECGconduction sequences (present in Patient Group B—those individuals witha variety of non-SCD related conduction defects arising from a multitudeof cardiac diseases, e.g., ischemic and non-ischemic). These isolatedsequences, as a result of their method of isolation, correlate with SCDrisk. These sequences are, by definition, SCD risk sequences.

The selection of the particular group of SCD risk sequences used toquantitatively identify individuals at risk for SCD with greatestsensitivity and specificity (e.g., >90%) is determined and constructedusing local and global optimization methods such as in an optimizationprocess or processing component (e.g., Local/Global optimization subunit184). For example, the optimization methods implemented may include oneor more of genetic, multi-objective, and annealing optimizationtechniques. This optimized SCD risk sequence ensemble is the output ofthe SCD risk sequence Isolator. This sequence ensemble is used topreprogram the SCD risk Predictor (FIG. 9).

3. SCD Risk Predictor

The function of the SCD risk predictor 104 is to quantitativelydetermine the risk in any given individual of the occurrence of SCD. TheSCD risk predictor 104 is preprogrammed (with the optimized SCD risksequence ensemble) by the SCD risk sequence isolator. Oncepreprogrammed, the SCD risk predictor is fully functional and able todetermine the SCD risk of any individual in a clinical setting. The SCDrisk predictor may use as input the digital ECG data obtained from anystandard, resting 12-lead ECG device.

The SCD risk predictor 104 functions by determining the alignment(similarity) scores obtained by aligning the test patient digital ECGdata to the optimized SCD risk sequence ensembles. The actual SCD riskis quantitatively determined from the number of SCD risk sequencespresent in the test patient, and by the corresponding alignment scores.

As illustrated in FIG. 9, the predictor 104 may include a preprocessor190 identical to the preprocessor 160 of the SCD risk sequence isolator102. The preprocessor 190 may receive as input the 12-lead restingdigital ECG data obtained from any individual for whom the calculationof SCD risk is desired. This preprocessor 190 denoises andcalculus-transforms the individual's input digital ECG data. (This isidentical to the operation of the SCD risk sequence isolator 102.)

With continued reference to FIG. 9, in a manner analogous to theoperation of the SCD risk sequence isolator's processor 160, theprocessor 192 of the SCD risk predictor 104 determines the alignmentscore for every SCD risk sequence with its corresponding digital ECGsequence (of the same lead and calculus transform) of the digital ECGdata of the individual being tested.

Based upon the number of risk sequences present in the test patient andthe alignment score determined for each SCD risk sequence, the relativerisk of the study patient for experiencing SCD is quantitativelydetermined.

Finally, the SCD risk predictor may graphically demonstrate the location(and corresponding alignment score) of any and all SCD risk sequencespresent within the digital ECG of the test individual.

For example, the Predictor may be configured with a processor tocalculate an individual's SCD risk score by determining the alignmentscore between each SCD risk sequence in the risk sequence ensemblestored in its memory (and obtained previously from the Isolator) and theECG sequences present in the individual being tested.

In some cases, the output of the predictor may be a number ranging fromzero to one. In such an example, SCD risk scores correlate with SCD riskas follows:

SCD RISK SCORE SCD RISK 0.00-0.10 very low risk 0.11-0.40 low risk0.41-0.70 moderate risk 0.71-1.00 high risk

Although the output may be a real number, in some versions an index maybe implemented on a suitable scale for a similar stratification of therisk. Similarly, the output may include a message such as textidentifying the nature of the risk (e.g., very low, low, moderate, highetc.). Other formats for stratification may optionally be implemented.

4. Other Implementations

The implementations of each of the isolator 102 and predictor 104including the processes, parts, units and subunits thereof are merelyillustrative, and not meant to be limiting. Each apparatus may includeother parts, units, subunits, or variations thereof. For instance, eachof the SCD risk sequence isolator 102 and the SCD risk predictor 104 maybe divided into additional parts, units, or subunits.

According to some aspects of the technology, the predictor 104, alone orin combination with other subunits, may be a plug-in application to astandard ECG device, such as a standard resting 12-lead ECG. The SCDrisk sequences may be pre-stored in the predictor 104. Moreover, theprocesses and methods described herein may be performed in whole or inpart by a computer or other processing apparatus that may includeintegrated chips, a memory and/or other control instruction, data orinformation storage medium. For example, programmed instructionsencompassing such methodologies may be coded on integrated chips in thememory of the device. Such instructions may also or alternatively beloaded as software or firmware using an appropriate data storage medium.With such an apparatus, the device can determine and analyze digital ECGsequences from previously measured and received digital ECG data, suchas data measured by a discrete measuring device.

In some cases, the predictor may be part of an ATP-ICD (anti-tachycardiapacing (ATP) ICD). In some cases, a predictor may be coupled to adefibrillator, e.g., by wireless communication, so as to receive EGMdata for testing purposes. Thus, while a 12-lead ECG has previously beendescribed, the predictor and/or isolator may be configured to operate on3-lead EGM signals or any other number of leads or electrodemeasurements.

The SCD risk sequence ensemble may be constructed by an SCD risksequence isolator similar to the one described in detail herein, butusing digital EGM data rather than digital ECG data as input. This wouldresult in the creation of an EGM-specific SCD risk sequence ensemble.This ensemble could then be used to train an appropriate SCD riskpredictor unit. Such an SCD risk predictor could be incorporated into anATP-ICD device to enable anti-tachycardia pacing (ATP) prior to theonset of SCD (SCA), thereby preventing any occurrence of ventriculartachycardia are ventricular fibrillation.

In addition, the SCD risk predictor, which functions in real time, couldbe used to guide ventricular ablation procedures. The predictor coulddetermine at the time of an ablation procedure (ventricular ablationperformed to lower the incidence of SCD in patients at risk for SCD)whether the patient risk for SCD has been successfully reduced and theprocedure can be ended. At present, electrical inducibility ofventricular tachycardia is the method used to predict the success ofventricular ablation. This technique has not been demonstrated to be agood predictor of SCD.

5. Some Potential Advantages of the Present Technology

The present technology for identifying SCD risk sequences and predictingand quantifying an individual's risk for SCD has many advantages.

First, the present technology may provide noninvasive riskstratification in individuals. There is, at present, no invasive ornoninvasive method for quantifying SCD risk.

Second, the device may identify and isolate critical information hiddenwithin complex data outputs/collections. It may identify and isolatedigital electrocardiogram sequences responsible or otherwise associatedwith the onset of Sudden Cardiac Death (such as those measured within aresting, multi-lead digital ECG).

Third, the present technology may perform risk-stratification inindividuals of all risk levels, including no risk, low risk,intermediate risk and high risk. In particular, the present technologymay identify individuals at risk for SCD that are not detectable byprior known techniques.

Fourth, the present technology may identify individuals at risk for SCDwith high specificity and sensitivity levels not achieved before.

Fifth, the present technology may do so without use of knownfactors—alone or in combination—presently used in SCDrisk-stratification, including left ventricular ejection fraction(LVEF), signal-averaged electrocardiogram (SAECG), microvolt T-wavealternans (MTWA), ambulatory ECG monitoring, heart failure, metabolicfactors and autonomic control. As such, the present technology obviatesthe shortcomings of these technologies as discussed in the backgroundsection, although in some embodiments the assessment may be combinedwith known methods.

Sixth, by identifying individuals at risk for SCD, the presenttechnology has a transformational impact in the initiation ofappropriate treatment of SCD (e.g., ICD implantation) and thereby maygreatly reduce the incidence of SCD.

Seventh, the present technology poses no risk to any individual, otherthan the insignificant risk of undergoing a standard ECG.

Eighth, the present technology may successfully calculate SCD risk inall individuals, regardless of whether the individuals have experiencedcardiac surgery, and regardless of their clinical history, includinghistory of myocardial infarction, atherosclerotic heart disease,cardiomyopathy, cardiac rhythm, and cardiac condition abnormalities. Forexample, unlike most of the presently available risk-stratificationtechnologies, the technology described herein can be performed and usedto determine SCD risk in individuals with common cardiac rhythmdisorders, including atrial fibrillation, premature ventricularcontractions (PVCs), as well as bundle branch blocks and complete heartblock.

Ninth, the present technology may be used in individuals with no knownrisk factors for SCD. This includes athletes at the middle school, highschool, college and professional level, relatives of individuals whohave experienced SCD as well as part of any individuals undergoing aroutine physical exam.

Tenth, the present technology may be used to follow the progression ofSCD risk in any individual.

Finally, the general principles used in the technology described hereinmay be used to extract important parameters and information from a vastvariety of signals, including speech, sound, graphic, visual,mechanical, and electrical devices.

7. Conclusion

Although aspects of the disclosure herein have been described withreference to particular embodiments, it is to be understood that theseembodiments are merely illustrative of the principles and applicationsof the present disclosure. It is therefore to be understood thatnumerous modifications may be made to the illustrative embodiments andthat other arrangements may be devised without departing from the spiritand scope of the present disclosure as defined by the appended claims.

The invention claimed is:
 1. A method for determining an individual'srisk for sudden cardiac death (SCD), the method comprising: receiving adigital electrocardiogram (ECG) measurement taken from the individual;and quantitatively determining, in a processor, in the received ECGmeasurement, presences of predetermined digital electrocardiogram (ECG)sequences indicative of a risk for SCD, wherein the processor assessesquality of alignment between (a) the predetermined digital ECG sequencesand derivatives of the predetermined ECG sequences and (b) ECG sequencesmeasured from the individual, and derivatives of the ECG sequencesmeasured from the individual.
 2. The method of claim 1, wherein theprocessor detects presence of a first predetermined ECG sequence of thepredetermined ECG sequences by computing an alignment error scorerelated to the first predetermined ECG sequence.
 3. The method of claim2, wherein the ECG measurement is obtained from a standard restingmulti-lead ECG machine.
 4. The method of claim 3, wherein the processordetects, in the received ECG measurement, the first predetermineddigital ECG sequence indicative of the risk for SCD by: receiving thefirst predetermined digital ECG sequence indicative of the risk for SCD;aligning the first predetermined digital ECG sequence with respect to anECG sequence obtained from the received ECG measurement, to obtain analignment; determining an error score for the alignment; anddetermining, in the received ECG measurement, presence of the firstpredetermined digital ECG sequence indicative of the risk for SCD basedon the error score.
 5. The method of claim 3, further comprisingpreprocessing, with a preprocessor, the ECG measurement taken from theindividual to obtain first and second derivatives of digital ECG datafrom the ECG measurement.
 6. The method of claim 5, wherein thepreprocessor preprocesses the digital ECG measurement taken from theindividual by: denoising the digital ECG data, and deriving first andsecond derivatives from the denoised digital ECG data.
 7. The method ofclaim 6, wherein the ECG measurement is denoised by at least one of aFinite Impulse Response (FIR) filter and a wavelet packet operationusing entropy calculations to optimize threshold settings.
 8. The methodof claim 7, wherein the noise includes at least one of electrical noise,mechanical noise, respiratory artifacts, white noise, movement artifact,and baseline drift.
 9. The method of claim 7, wherein the processordetects, in the received digital ECG measurement, the predetermineddigital ECG sequences indicative of the risk for SCD by: receiving thepredetermined digital ECG sequences indicative of the risk for SCD;aligning ECG sequences from the digital ECG measurement with thepredetermined digital ECG sequences associated with SCD risk, to obtainan alignment; and determining an error score for the alignment; anddetermining, in the received digital ECG measurement, the presence ofthe first predetermined digital ECG sequence indicative of the risk forSCD based upon the error score.
 10. An apparatus comprised of: a storagedevice containing an optimized ensemble of sudden cardiac death (SCD)risk sequences, wherein SCD risk sequences are determined by assessingquality of alignment of ECG sequences and derivatives of the ECGsequences between multiple individuals.
 11. The apparatus of claim 10,further comprising one or more processors, wherein digital ECGmeasurements of a test patient are preprocessed to: denoise the ECGmeasurements to obtain denoised digital ECG data, and derive first andsecond derivatives of the denoised digital ECG data.
 12. The apparatusof claim 11, further comprising at least one of a Finite ImpulseResponse (FIR) digital filter and a wavelet packet denoising processwhose threshold settings are optimally determined by entropycalculations.
 13. The apparatus of claim 12, wherein removed noiseincludes at least one of electrical noise, mechanical noise, respirationartifacts, white noise, movement artifact, and baseline drift.
 14. Theapparatus of claim 12, wherein the digital ECG measurement from the testpatient is obtained from a standard resting multi-lead ECG machine. 15.The apparatus of claim 12, wherein a processor of the one or moreprocessors is configured to align the denoised digital ECG data from thetest individual with the optimized ensemble of SCD risk sequences. 16.An apparatus for determining an individual's risk for sudden cardiacdeath (SCD), the apparatus comprising: a storage device storing an SCDrisk sequence ensemble, wherein SCD risk sequences of the ensemble aredetermined by assessing quality of alignment of ECG sequences andderivatives of the ECG sequences between multiple individuals; and aprocessor in communication with the storage device configured to:receive a digital ECG measurement taken from a test individual; detectin the test patient digital ECG measurement any predeterminedelectrocardiogram (ECG) sequences indicative of a risk for SCD based onthe SCD risk sequences in the SCD risk sequence ensemble; and determinequantitative risk for the occurrence of SCD in the individual based onthe detection of the predetermined ECG sequences and their alignment.